Improved Iterative Detection of Multiuser Signals with Fast Frequency-Hopping Modulation
Bibliographic record
Abstract
An improved iterative receiver is developed for multiuser communications using fast frequency-hopping modulation. Each user employs a channel encoder to protect its information and facilitate interference cancellation at the receiver. At the destination, in order to reliably extract signals from all users, the detection algorithm employs double iteration process: an outer iteration between the interference canceler and soft-input soft-output (SISO) decoder, and an inner iteration between the soft demapper and the SISO decoder. The proposed detection algorithm works with direct as well as relay-aided transmissions. Two relay scenarios are investigated, which are amplify-and-forward and partial-decode-and-forward relaying. Under the same spectral efficiency, simulation results demonstrate the excellent performance of the proposed receiver when compared to the performance of single iterative receiver and other previously-proposed interference cancellation schemes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".